Title

Author

Date of Award

Spring 1-1-2011

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Geography

First Advisor

Barbara P. Buttenfield

Second Advisor

Lauren E. Hay

Third Advisor

Steven D. Prager

Abstract

The author designs and implements an approach that exploits semantically important information that is not ordinarily included in traditional information retrieval approaches to improve the handling of Geographic Information System (GIS) procedural software. In this approach, what are termed here implicit keywords, descriptors designed to recognize characteristics not explicitly recorded within the GIS procedure source code, are created and used in an automated, inductive process to organize a large set of GIS procedures to reveal meaningful groupings. The process uses the Self-Organizing Maps (SOM), a specialized artificial neural network, to create a two-dimensional representation of an input data set wherein topological properties of the input data set are preserved. Such maps are important tools for helping visualize, browse, filter, and evaluate a set of GIS procedures . Browsing, filtering, and evaluation help to improve human understanding of available GIS resources. By facilitating mechanisms for improved software sharing and exchange, the methods described here may guide future researchers in the selection of more appropriate procedures for a given task. Through experiments of this dissertation, the author demonstrates that while using GIS commands as explicit keywords can produce helpful organizations of GIS procedures, development of implicit keywords can be used to moderate, improve, and specialize the results of the explicit keyword process. The results of the different experiments not only show the impacts of applying different keyword schemes, but bear witness to the fact that GIS functionality can be organized with consistent methodological rigor in potentially very different ways to reprioritize specific types of functionality.